Untitled

#PM2.5 leaflets
PM25_1990_leaflet

PM25_2017_leaflet

DALY_1990_leaflet

DALY_2017_leaflet

DALY_change_leaflet

tempdata <- data_healthimp_wide[, c(1, 2, 3, 4, 5)]
tempdata <- tempdata%>%
  pivot_wider(values_from = `ambientpm2.5.dalyper1k`,
              names_from = sex,
              names_glue = "{sex}")
tempdata$DALY <- tempdata$`F` + tempdata$M

tempdata <- tempdata%>%
  select(-c(`F`, M))
tempdata <- tempdata[tempdata$year == 1990 | tempdata$year == 1995 | tempdata$year == 2000| tempdata$year == 2005| tempdata$year == 2010| tempdata$year == 2011| tempdata$year == 2012| tempdata$year == 2013| tempdata$year == 2014| tempdata$year == 2015| tempdata$year == 2016| tempdata$year == 2017, ]

path_bella <- "C:/Users/Bella/Desktop/git/Blog-HealthAndJusticeLeague/data"
data_PM25 <- read_csv(paste0(path_bella,
                             "/Exposure_PM25_air.csv"))

## Warning: 10 parsing failures.
##   row        col           expected          actual                                                                                file
##  7627 Flag Codes 1/0/T/F/TRUE/FALSE E               'C:/Users/Bella/Desktop/git/Blog-HealthAndJusticeLeague/data/Exposure_PM25_air.csv'
##  7627 Flags      1/0/T/F/TRUE/FALSE Estimated value 'C:/Users/Bella/Desktop/git/Blog-HealthAndJusticeLeague/data/Exposure_PM25_air.csv'
##  7628 Flag Codes 1/0/T/F/TRUE/FALSE E               'C:/Users/Bella/Desktop/git/Blog-HealthAndJusticeLeague/data/Exposure_PM25_air.csv'
##  7628 Flags      1/0/T/F/TRUE/FALSE Estimated value 'C:/Users/Bella/Desktop/git/Blog-HealthAndJusticeLeague/data/Exposure_PM25_air.csv'
## 10263 Flag Codes 1/0/T/F/TRUE/FALSE E               'C:/Users/Bella/Desktop/git/Blog-HealthAndJusticeLeague/data/Exposure_PM25_air.csv'
## ..... .......... .................. ............... ...................................................................................
## See problems(...) for more details.

#taking out unnecessary column varaibles
data_PM25 <- data_PM25%>%
  select(COU, Country, Variable, Year, Value)%>%
  rename(country = Country,
         year = Year)

#separate out the variables
data_PM25_wide <- data_PM25%>%
  pivot_wider(values_from = Value,
              names_from = Variable,
              names_glue = "{Variable}")
tempdata2 <- left_join(tempdata, data_PM25_wide, by = c("country", "year"), copy = TRUE)%>%
  select(-c(COU, cou))

tempdata3 <- tempdata2[, -c(5, 6, 7, 8)]

tempdata3 <- tempdata3%>%
  pivot_longer(-c(year, country),
               names_to = "measure_type", values_to = "value")

ggplot(data = tempdata3, mapping = aes(x = year, y = value, color = as.factor(measure_type))) +
    geom_point() +
    labs(
      title = "TITLE",
      subtitle = "1990 to 2017",
      y = "Value",
      x = "Year")

## Warning: Removed 216 rows containing missing values (geom_point).

#trying out sth different in the meantime
library(ggplot2)
library(gganimate)
library(gifski)


tempdata <- data_healthimp_wide[, c(1, 2, 3, 4, 5)]
tempdata <- tempdata%>%
  pivot_wider(values_from = `ambientpm2.5.dalyper1k`,
              names_from = sex,
              names_glue = "{sex}")
tempdata$DALY <- tempdata$`F` + tempdata$M

tempdata <- tempdata%>%
  select(-c(`F`, M))
tempdata <- tempdata[tempdata$year == 1990 | tempdata$year == 1995 | tempdata$year == 2000| tempdata$year == 2005| tempdata$year == 2010| tempdata$year == 2011| tempdata$year == 2012| tempdata$year == 2013| tempdata$year == 2014| tempdata$year == 2015| tempdata$year == 2016| tempdata$year == 2017, ]

path_bella <- "C:/Users/Bella/Desktop/git/Blog-HealthAndJusticeLeague/data"
data_PM25 <- read_csv(paste0(path_bella,
                             "/Exposure_PM25_air.csv"))

#taking out unnecessary column varaibles
data_PM25 <- data_PM25%>%
  select(COU, Country, Variable, Year, Value)%>%
  rename(country = Country,
         year = Year)

#separate out the variables
data_PM25_wide <- data_PM25%>%
  pivot_wider(values_from = Value,
              names_from = Variable,
              names_glue = "{Variable}")
tempdata2 <- left_join(tempdata, data_PM25_wide, by = c("country", "year"), copy = TRUE)%>%
  select(-c(COU, cou))


g <- ggplot(tempdata2, aes(`Mean population exposure to PM2.5`, DALY, color = country))+
  geom_point(alpha = 0.7, show.legend = FALSE)+
  scale_size(range = c(2, 12))+
  scale_x_continuous(trans = 'log10')+
  scale_y_continuous(trans = 'log10')+
  labs(title = 'Year: {frame_time}', x = 'PM 2.5', y = 'DALY per 1K')+
  transition_time(year)+
  ease_aes('linear')

animate(g, duration = 10, fps = 20, width = 400, height = 400, renderer = gifski_renderer())

anim_save("tempdata2.gif")